{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceForensics++"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we show the results for FaceForensics++. You can create from scratch the features or use the pre-computed ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Create feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to create the features, first of all download the \"prepro_deepFake\" folder from [link](https://bit.ly/2wkPZYv). Be sure to save the folder together with this notebook. \n",
    "\n",
    "Otherwise, just jump to section 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA Saved\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import radialProfile\n",
    "import glob\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "data= {}\n",
    "epsilon = 1e-8\n",
    "N = 300\n",
    "y = []\n",
    "error = []\n",
    "\n",
    "\n",
    "number_iter = 1600\n",
    "\n",
    "psd1D_total = np.zeros([number_iter, N])\n",
    "label_total = np.zeros([number_iter])\n",
    "psd1D_org_mean = np.zeros(N)\n",
    "psd1D_org_std = np.zeros(N)\n",
    "\n",
    "\n",
    "cont = 0\n",
    "\n",
    "#fake data\n",
    "rootdir = 'prepro_deepFake/fake/'\n",
    "\n",
    "for subdir, dirs, files in os.walk(rootdir):\n",
    "    for file in files:        \n",
    "\n",
    "        filename = os.path.join(subdir, file)\n",
    "        \n",
    "        img = cv2.imread(filename,0)\n",
    "        \n",
    "        # we crop the center\n",
    "        h = int(img.shape[0]/3)\n",
    "        w = int(img.shape[1]/3)\n",
    "        img = img[h:-h,w:-w]\n",
    "\n",
    "        f = np.fft.fft2(img)\n",
    "        fshift = np.fft.fftshift(f)\n",
    "\n",
    "        magnitude_spectrum = 20*np.log(np.abs(fshift))\n",
    "        psd1D = radialProfile.azimuthalAverage(magnitude_spectrum)\n",
    "\n",
    "        # Calculate the azimuthally averaged 1D power spectrum\n",
    "        points = np.linspace(0,N,num=psd1D.size) # coordinates of a\n",
    "        xi = np.linspace(0,N,num=N) # coordinates for interpolation\n",
    "\n",
    "        interpolated = griddata(points,psd1D,xi,method='cubic')\n",
    "        interpolated /= interpolated[0]\n",
    "\n",
    "        psd1D_total[cont,:] = interpolated             \n",
    "        label_total[cont] = 0\n",
    "        cont+=1\n",
    "\n",
    "        if cont == number_iter:\n",
    "            break\n",
    "    if cont == number_iter:\n",
    "        break\n",
    "            \n",
    "for x in range(N):\n",
    "    psd1D_org_mean[x] = np.mean(psd1D_total[:,x])\n",
    "    psd1D_org_std[x]= np.std(psd1D_total[:,x])\n",
    "\n",
    "\n",
    "## real data\n",
    "psd1D_total2 = np.zeros([number_iter, N])\n",
    "label_total2 = np.zeros([number_iter])\n",
    "psd1D_org_mean2 = np.zeros(N)\n",
    "psd1D_org_std2 = np.zeros(N)\n",
    "\n",
    "\n",
    "cont = 0\n",
    "rootdir2 = 'prepro_deepFake/real/'\n",
    "\n",
    "for subdir, dirs, files in os.walk(rootdir2):\n",
    "    for file in files:        \n",
    "\n",
    "        filename = os.path.join(subdir, file)\n",
    "        parts = filename.split(\"/\")\n",
    "   \n",
    "        img = cv2.imread(filename,0)\n",
    "    \n",
    "        # we crop the center\n",
    "        h = int(img.shape[0]/3)\n",
    "        w = int(img.shape[1]/3)\n",
    "        img = img[h:-h,w:-w]\n",
    "\n",
    "        f = np.fft.fft2(img)\n",
    "        fshift = np.fft.fftshift(f)\n",
    "        fshift += epsilon\n",
    "\n",
    "\n",
    "        magnitude_spectrum = 20*np.log(np.abs(fshift))\n",
    "\n",
    "        # Calculate the azimuthally averaged 1D power spectrum\n",
    "        psd1D = radialProfile.azimuthalAverage(magnitude_spectrum)\n",
    "\n",
    "        points = np.linspace(0,N,num=psd1D.size) # coordinates of a\n",
    "        xi = np.linspace(0,N,num=N) # coordinates for interpolation\n",
    "\n",
    "        interpolated = griddata(points,psd1D,xi,method='cubic')\n",
    "        interpolated /= interpolated[0]\n",
    "\n",
    "        psd1D_total2[cont,:] = interpolated             \n",
    "        label_total2[cont] = 1\n",
    "        cont+=1\n",
    "\n",
    "        if cont == number_iter:\n",
    "            break\n",
    "    if cont == number_iter:\n",
    "        break\n",
    "\n",
    "for x in range(N):\n",
    "    psd1D_org_mean2[x] = np.mean(psd1D_total2[:,x])\n",
    "    psd1D_org_std2[x]= np.std(psd1D_total2[:,x])\n",
    "    \n",
    "    \n",
    "y.append(psd1D_org_mean)\n",
    "y.append(psd1D_org_mean2)\n",
    "\n",
    "error.append(psd1D_org_std)\n",
    "error.append(psd1D_org_std2)\n",
    "\n",
    "psd1D_total_final = np.concatenate((psd1D_total,psd1D_total2), axis=0)\n",
    "label_total_final = np.concatenate((label_total,label_total2), axis=0)\n",
    "\n",
    "data[\"data\"] = psd1D_total_final\n",
    "data[\"label\"] = label_total_final\n",
    "\n",
    "output = open('train_3200.pkl', 'wb')\n",
    "pickle.dump(data, output)\n",
    "output.close()\n",
    "\n",
    "print(\"DATA Saved\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Loading Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we load the features. Either the pre-computed ones or the features that you have created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "# load feature file\n",
    "pkl_file = open('train_3200.pkl', 'rb')\n",
    "data = pickle.load(pkl_file)\n",
    "pkl_file.close()\n",
    "X = data[\"data\"]\n",
    "y = data[\"label\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at the label distribution, to be sure that we have a balanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdd15107cc0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Check Spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We havw a look to the spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Power Spectrum')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = int(X.shape[0]/2)\n",
    "num_feat = X.shape[1]\n",
    "\n",
    "psd1D_org_0 = np.zeros((num,num_feat))\n",
    "psd1D_org_1 = np.zeros((num,num_feat))\n",
    "psd1D_org_0_mean = np.zeros(num_feat)\n",
    "psd1D_org_0_std = np.zeros(num_feat)\n",
    "psd1D_org_1_mean = np.zeros(num_feat)\n",
    "psd1D_org_1_std = np.zeros(num_feat)\n",
    "\n",
    "cont_0=0\n",
    "cont_1=0\n",
    "\n",
    "# We separate real and fake using the label\n",
    "for x in range(X.shape[0]):\n",
    "    if y[x]==0:\n",
    "        psd1D_org_0[cont_0,:] = X[x,:]\n",
    "        cont_0+=1\n",
    "    elif y[x]==1:\n",
    "        psd1D_org_1[cont_1,:] = X[x,:]\n",
    "        cont_1+=1\n",
    "\n",
    "# We compute statistcis\n",
    "for x in range(num_feat):\n",
    "    psd1D_org_0_mean[x] = np.mean(psd1D_org_0[:,x])\n",
    "    psd1D_org_0_std[x]= np.std(psd1D_org_0[:,x])\n",
    "    psd1D_org_1_mean[x] = np.mean(psd1D_org_1[:,x])\n",
    "    psd1D_org_1_std[x]= np.std(psd1D_org_1[:,x])\n",
    "    \n",
    "# Plot\n",
    "x = np.arange(0, num_feat, 1)\n",
    "fig, ax = plt.subplots(figsize=(15, 9))\n",
    "ax.plot(x, psd1D_org_0_mean, alpha=0.5, color='red', label='Fake', linewidth =2.0)\n",
    "ax.fill_between(x, psd1D_org_0_mean - psd1D_org_0_std, psd1D_org_0_mean + psd1D_org_0_std, color='red', alpha=0.2)\n",
    "ax.plot(x, psd1D_org_1_mean, alpha=0.5, color='blue', label='Real', linewidth =2.0)\n",
    "ax.fill_between(x, psd1D_org_1_mean - psd1D_org_1_std, psd1D_org_1_mean + psd1D_org_1_std, color='blue', alpha=0.2)\n",
    "plt.tick_params(axis='x', labelsize=20)\n",
    "plt.tick_params(axis='y', labelsize=20)\n",
    "ax.legend(loc='best', prop={'size': 20})\n",
    "plt.xlabel(\"Spatial Frequency\", fontsize=20)\n",
    "plt.ylabel(\"Power Spectrum\", fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we classify using the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM: 0.8605\n",
      "LR: 0.7865\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import pickle\n",
    "\n",
    "#train\n",
    "pkl_file = open('train_3200.pkl', 'rb')\n",
    "data = pickle.load(pkl_file)\n",
    "pkl_file.close()\n",
    "X = data[\"data\"]\n",
    "y = data[\"label\"]\n",
    "\n",
    "svclassifier_r = SVC(C=6.37, kernel='rbf', gamma=0.86)\n",
    "svclassifier_r.fit(X, y)\n",
    "logreg = LogisticRegression(solver='liblinear', max_iter=1000)\n",
    "logreg.fit(X, y)\n",
    "\n",
    "#test\n",
    "pkl_file = open('test_1000.pkl', 'rb')\n",
    "data = pickle.load(pkl_file)\n",
    "pkl_file.close()\n",
    "X_ = data[\"data\"]\n",
    "y_ = data[\"label\"]\n",
    "\n",
    "SVM = svclassifier_r.score(X_, y_)\n",
    "LR = logreg.score(X_, y_)\n",
    "\n",
    "\n",
    "print(\"SVM: \"+str(SVM))\n",
    "print(\"LR: \"+str(LR))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
